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Coal fires are a global environmental and social catastrophe, which deteriorate air quality, destroy the surface vegetation and soil, and pollute groundwater. The delineation of coal fire scope and the location of the fire source are indispensable for the efficient control of them. In this paper, three different geophysical methods, including magnetic method, self-potential method, and electrical resistivity tomography (ERT), are adopted to detect coal fires. Upward continuation is conducted with magnetic data and self-potential data to highlight the net anomaly due to coal fires and to suppress the noise from the background geomagnetic field and geoelectric field. The scope of the fires is delineated by magnetic and self-potential anomalies in 2D and is compared with the borehole temperature data. Furthermore, self-potential data is inverted by an iterative compaction constrained inversion algorithm with the assistance of resistivity data. The inverted fire source location, which is represented by the source current density, is consistent with field survey results. The findings are critical to determine the scope and source of underground coal fires. [ABSTRACT FROM AUTHOR]

Underground coal fire is a global geological disaster that causes the loss of resources as well as environmental pollution. Xinjiang, China, is one of the regions suffering from serious underground coal fires. The accurate monitoring of underground coal fires is critical for management and extinguishment, and many remote sensing-based approaches have been developed for monitoring over large areas. Among them, the multi-temporal interferometric synthetic aperture radar (MT-InSAR) techniques have been recently employed for underground coal fires-related ground deformation monitoring. However, MT-InSAR involves a relatively high computational cost, especially when the monitoring area is large. We propose to use a more cost-efficient Stacking-InSAR technique to monitor ground deformation over underground coal fire areas in this study. Considering the effects of atmosphere on Stacking-InSAR, an ERA5 data-based estimation model is employed to mitigate the atmospheric phase of interferograms before stacking. Thus, an adaptive ERA5-Corrected Stacking-InSAR method is proposed in this study, and it is tested over the Fukang coal fire area in Xinjiang, China. Based on original and corrected interferograms, four groups of ground deformation results were obtained, and the possible coal fire areas were identified. In this paper, the ERA5 atmospheric delay products based on the estimation model along the LOS direction (D-LOS) effectively mitigate the atmospheric phase. The accuracy of ground deformation monitoring over a coal fire area has been improved by the proposed method choosing interferograms adaptively for stacking. The proposed Adaptive ERA5-Corrected Stacking-InSAR method can be used for efficient ground deformation monitoring over large coal fire areas. [ABSTRACT FROM AUTHOR]